Paper Info
Title
Dynamic Thermal Prediction Model For The Electronic Equipment Cabin Based On Rvfl Network
Abstract
Accurate modeling of heat transfer devices is important for airborne electronic equipment cabin thermal prediction and thermal management. The current thermal models are mostly lumped model based on Thermal Network Model (TNM). The least square method is often used to compute and identify its parameters. Because of the principle of lumped parameter, thermal network model cannot correctly characterize the nonlinear temperature change process in the cabin, and the prediction accuracy is poor. Recently, neural network has gradually become a major research direction of heat transfer process modeling due to its powerful learning ability and data approximation performance. In order to achieve more accurate online thermal modeling of electronic equipment cabin, a sliding time window method based on Random Vector Function Link (RVFL) is proposed. By training the measured temperature in the electronic equipment cabin, the sliding window RVFLNN is built to predict the temperature of the equipment in the subsequent window. When the accuracy of this method cannot meet the requirement, the model is quickly updated according to the data acquired in real time. The real data experiments verify the effectiveness of this method as well as fast modeling speed.
Year
DOI
Venue
2018
10.1109/CISP-BMEI.2018.8633235
2018 11TH INTERNATIONAL CONGRESS ON IMAGE AND SIGNAL PROCESSING, BIOMEDICAL ENGINEERING AND INFORMATICS (CISP-BMEI 2018)
Keywords
Field
DocType
electronic equipment cabin, sliding time window method, RVFLNN
Data modeling,Computer vision,Nonlinear system,Sliding window protocol,Computer science,Simulation,Temperature control,Heat transfer,Process modeling,Atmospheric model,Artificial intelligence,Artificial neural network
Conference
Citations 
PageRank 
References 
0
0.34
0
Authors
4
Name
Order
Citations
PageRank
Zhiyong Sheng101.69
Zhiqiang Zeng213916.35
Qing Tian313.81
YanPing Wang44819.44